sources of news and other data
sources to understand media
perception of stocks or companies.

AllianceBernstein, meanwhile, has
been focusing on techniques to
learn why a particular sentiment
might arise, using machine learning techniques.

But not everyone is convinced
about the market’s readiness for
machine learning. The rules of the
finance game are not constant like
chess or Go. At the same time data
financial datasets are much noisier
than those typical of the applications where machine learning
has worked best so far. These
days financial data is growing in
diversity, consisting not only of
the obvious numbers and text but
also more unusual information
sources, ranging from meteorological diagrams and weather forecasts
to social media. While it is vastly
cheaper to access and store financial data the challenge of using
it under these strictures remains
tough. And, of course, getting an
AI system to handle the quirks of
the markets which are impacted
by human psychology, is another
question altogether. But these are
all surmountable challenges. It
seems that the market is edging
ever closer to the day when the
machines will be investing for us.

funds in the market. Despite the
backing, Quantopian is opening its
arms to a more universal, less elite,
investment audience. Contributors
to Quantopian come from diverse
backgrounds ranging from data
science to financial engineering,
software development to chemistry
and academia. Each author is paid

10% of the algorithm’s net profits,
while retaining ownership of the
intellectual property.

The next frontier

Last month the company started
to deploy its first tranches of seed
capital into some 20 strategies,
with each algorithm being allocated up to $3 million—the intention
is to build this up to $50 million
by the end of the year. Thomas
Wiecki, director of data science
at Quantopian, says the investment market is moving towards
machines. In particular towards
deep learning processes. The latter
is applied to algorithms which can
discover patterns and complex
relationships without being told
what to look for.

“Machine learning can process
and correlate huge amounts of data
to identify predictive signals—that
definitely provides the edge when
it comes to finance,” says Wiecki.

“While there are many examples ofusing machine learning in a tradingalgorithm on Quantopian, deeplearning is the next frontier. Deeplearning can successfully learncomplex features directly from theraw data and build higher-orderconnections something which isunfeasible for hand engineeringwhere the number of potentialinputs is huge.”The application of deep learningin quant finance is still new—butparticipants believe it could bethe next step in the evolutionof machine investing. Investorshave also been looking to use thetechnology for a number of otherinvestment purposes. Earlier thisyear Axa Investment Managerslaunched a nine-month trial to testthe machine learning technologyof MKT MediaStats which runsa sentiment analysis service forinvestors, scouring some 30,000

“Everyone around the world wants to havemachine learning incorporated into thebusiness.”